At the bottom of part 3 is a brief note about how to interpret the graphs contained in this series. The data presented here should be considered “for fun”. These will introduce a large amount of bias an uncertainty in the data. People are likely to mis-report their age. For example, some people identifying as female here may actually be male, and vice-versa. In addition, we cannot have any confidence that the people filling these out are who they say they are, and are actually interested in what they say they are interested in. Third, we should remember here that these data reflect what people fantasize about, not what they may want in reality. Second, this sample is likely to be very unrepresentative of the broader population, and so should not be overly generalized. There will almost certainly be errors and omissions. First, I am not a statistician, and this is not a rigorous statistical analysis. What follows, arranged loosely into sections, is my attempt to make sense of this data and to see what (if anything) interesting can be learned from it.īefore we begin, I would like to stipulate three brief disclaimers. From the title of the post which contains the kinklist, we can usually (but not always) find the user’s self-reported age and gender (see the note below on data privacy).Īs of June 29th, 2019, I have scraped 2464 profiles. There are 202 separate categories in the standard kinklist (although longer and shorter variations exist, they were excluded from this analysis), leading to a vector of length 202 which defines the interests of an individual user. Once we have these images we can use image processing software ( ImageJ) along with some custom scripts to automatically extract the colors for each item and map them to a five point scale, from 1 (no) to 5 (favorite), with omitted values being ignored for the purposes of analysis. We can scrape all of these that we find on Reddit using the Reddit API and download the associated image files (usually hosted on imgur) from the following subreddits, where they are commonly found ( dppprofiles, dirtypenpals, exxxchange). This data, if aggregated on a large enough scale, might be a good opportunity to explore what people are interested in, and what they are not. Figure 1.2: A completed kinklist for the author.
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